Overview

Dataset statistics

Number of variables20
Number of observations2353651
Missing cells519
Missing cells (%)< 0.1%
Duplicate rows4501
Duplicate rows (%)0.2%
Total size in memory359.1 MiB
Average record size in memory160.0 B

Variable types

NUM11
BOOL5
CAT4

Warnings

Dataset has 4501 (0.2%) duplicate rows Duplicates
PJ_UF is highly correlated with PF_UFHigh correlation
PF_UF is highly correlated with PJ_UFHigh correlation
PJ_IDADE_ABERTURA has 38132 (1.6%) zeros Zeros

Reproduction

Analysis started2020-09-18 01:16:49.731632
Analysis finished2020-09-18 01:25:15.682814
Duration8 minutes and 25.95 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

CPF
Real number (ℝ≥0)

Distinct1242061
Distinct (%)52.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.321659378e+10
Minimum1163
Maximum9.999999417e+10
Zeros0
Zeros (%)0.0%
Memory size18.0 MiB
2020-09-17T22:25:16.311066image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1163
5-th percentile897673140
Q14689856641
median1.454638265e+10
Q36.350147332e+10
95-th percentile9.233644227e+10
Maximum9.999999417e+10
Range9.999999301e+10
Interquartile range (IQR)5.881161668e+10

Descriptive statistics

Standard deviation3.272118334e+10
Coefficient of variation (CV)0.9850854531
Kurtosis-1.148330434
Mean3.321659378e+10
Median Absolute Deviation (MAD)1.348465013e+10
Skewness0.6310504199
Sum7.818026917e+16
Variance1.070675839e+21
MonotocityNot monotonic
2020-09-17T22:25:16.559801image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
596634307269< 0.1%
 
8.593706135e+10181< 0.1%
 
3077961326117< 0.1%
 
5.849690417e+10110< 0.1%
 
1.816014681e+1057< 0.1%
 
2.804770265e+1054< 0.1%
 
9.959913929e+1050< 0.1%
 
1.372061577e+1048< 0.1%
 
808628364048< 0.1%
 
993980473343< 0.1%
 
7.504062073e+1043< 0.1%
 
640047238041< 0.1%
 
699608570541< 0.1%
 
7.483854723e+1040< 0.1%
 
811043444439< 0.1%
 
5.359502569e+1038< 0.1%
 
7.654310227e+1038< 0.1%
 
541124277038< 0.1%
 
4.263765177e+1037< 0.1%
 
7.927082477e+1037< 0.1%
 
536327947937< 0.1%
 
1.80463648e+1037< 0.1%
 
104718870836< 0.1%
 
110363779736< 0.1%
 
8.626575076e+1035< 0.1%
 
Other values (1242036)235210199.9%
 
ValueCountFrequency (%) 
11631< 0.1%
 
19101< 0.1%
 
51501< 0.1%
 
412037< 0.1%
 
441301< 0.1%
 
805271< 0.1%
 
836231< 0.1%
 
856772< 0.1%
 
886921< 0.1%
 
1001451< 0.1%
 
ValueCountFrequency (%) 
9.999999417e+102< 0.1%
 
9.999989713e+101< 0.1%
 
9.999972277e+103< 0.1%
 
9.999932312e+101< 0.1%
 
9.99992851e+102< 0.1%
 
9.999897127e+102< 0.1%
 
9.999891217e+101< 0.1%
 
9.999880712e+103< 0.1%
 
9.999863737e+101< 0.1%
 
9.999862927e+101< 0.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.0 MiB
1
2320023 
0
 
33628
ValueCountFrequency (%) 
1232002398.6%
 
0336281.4%
 
2020-09-17T22:25:16.640617image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.0 MiB
1
2348843 
0
 
4808
ValueCountFrequency (%) 
1234884399.8%
 
048080.2%
 
2020-09-17T22:25:16.675491image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

PF_IDADE
Real number (ℝ≥0)

Distinct118
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.07646546
Minimum1
Maximum121
Zeros0
Zeros (%)0.0%
Memory size18.0 MiB
2020-09-17T22:25:16.749294image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25
Q133
median41
Q350
95-th percentile62
Maximum121
Range120
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.641799
Coefficient of variation (CV)0.2766819616
Kurtosis-0.2429688722
Mean42.07646546
Median Absolute Deviation (MAD)8
Skewness0.3970920001
Sum99033315
Variance135.531484
MonotocityNot monotonic
2020-09-17T22:25:16.881398image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
38816273.5%
 
39803893.4%
 
37769573.3%
 
35760603.2%
 
40754573.2%
 
41743803.2%
 
34739663.1%
 
36736333.1%
 
33725563.1%
 
42708903.0%
 
32703263.0%
 
43689022.9%
 
44662672.8%
 
31652462.8%
 
45625492.7%
 
46612292.6%
 
30582802.5%
 
47577632.5%
 
48575872.4%
 
29559312.4%
 
49558032.4%
 
50546472.3%
 
28508982.2%
 
51508782.2%
 
52502502.1%
 
Other values (93)71118030.2%
 
ValueCountFrequency (%) 
131< 0.1%
 
265< 0.1%
 
383< 0.1%
 
439< 0.1%
 
531< 0.1%
 
653< 0.1%
 
726< 0.1%
 
840< 0.1%
 
957< 0.1%
 
1047< 0.1%
 
ValueCountFrequency (%) 
1217< 0.1%
 
1207< 0.1%
 
1182< 0.1%
 
1163< 0.1%
 
1151< 0.1%
 
1142< 0.1%
 
1133< 0.1%
 
1122< 0.1%
 
1113< 0.1%
 
1106< 0.1%
 

PF_GENERO
Boolean

Distinct2
Distinct (%)< 0.1%
Missing519
Missing (%)< 0.1%
Memory size18.0 MiB
1
1184010 
0
1169122 
(Missing)
 
519
ValueCountFrequency (%) 
1118401050.3%
 
0116912249.7%
 
(Missing)519< 0.1%
 
2020-09-17T22:25:16.962723image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

PF_UF
Real number (ℝ≥0)

HIGH CORRELATION

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.775776868
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Memory size18.0 MiB
2020-09-17T22:25:17.024591image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q311
95-th percentile20
Maximum27
Range26
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.111339127
Coefficient of variation (CV)0.7859457943
Kurtosis-0.04350017686
Mean7.775776868
Median Absolute Deviation (MAD)4
Skewness0.9181389931
Sum18301465
Variance37.34846592
MonotocityNot monotonic
2020-09-17T22:25:17.124292image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%) 
132869514.0%
 
22055738.7%
 
31876248.0%
 
41776167.5%
 
51638557.0%
 
61622946.9%
 
71568486.7%
 
81335725.7%
 
91111384.7%
 
10845033.6%
 
11650722.8%
 
12646762.7%
 
13589462.5%
 
14567332.4%
 
15560282.4%
 
16541452.3%
 
17528092.2%
 
18464282.0%
 
19409481.7%
 
20373771.6%
 
21339841.4%
 
22220500.9%
 
23204030.9%
 
24187210.8%
 
2569770.3%
 
Other values (2)66360.3%
 
ValueCountFrequency (%) 
132869514.0%
 
22055738.7%
 
31876248.0%
 
41776167.5%
 
51638557.0%
 
61622946.9%
 
71568486.7%
 
81335725.7%
 
91111384.7%
 
10845033.6%
 
ValueCountFrequency (%) 
27954< 0.1%
 
2656820.2%
 
2569770.3%
 
24187210.8%
 
23204030.9%
 
22220500.9%
 
21339841.4%
 
20373771.6%
 
19409481.7%
 
18464282.0%
 

CNPJ
Real number (ℝ≥0)

Distinct1180405
Distinct (%)50.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.170408305e+13
Minimum455000107
Maximum9.7711797e+13
Zeros0
Zeros (%)0.0%
Memory size18.0 MiB
2020-09-17T22:25:17.785522image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum455000107
5-th percentile4.386387e+12
Q11.3872232e+13
median2.2026568e+13
Q32.9237713e+13
95-th percentile3.50093755e+13
Maximum9.7711797e+13
Range9.7711342e+13
Interquartile range (IQR)1.5365481e+13

Descriptive statistics

Standard deviation1.101441204e+13
Coefficient of variation (CV)0.5074811043
Kurtosis7.257497452
Mean2.170408305e+13
Median Absolute Deviation (MAD)7.666096e+12
Skewness1.310274707
Sum5.108383678e+19
Variance1.213172725e+26
MonotocityNot monotonic
2020-09-17T22:25:17.913222image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.379503e+12106< 0.1%
 
1.0609938e+13103< 0.1%
 
9.24845e+12100< 0.1%
 
7.715251e+1292< 0.1%
 
7.774211e+1291< 0.1%
 
5.021025e+1291< 0.1%
 
2.8016077e+1384< 0.1%
 
9.116860002e+1183< 0.1%
 
1.5334477e+1383< 0.1%
 
2.276269e+1382< 0.1%
 
5.586590002e+1180< 0.1%
 
1.0014536e+1377< 0.1%
 
5.605572e+1277< 0.1%
 
1.3265725e+1376< 0.1%
 
1.6808908e+1374< 0.1%
 
1.0627791e+1373< 0.1%
 
3.265392e+1273< 0.1%
 
1.2076338e+1368< 0.1%
 
1.8233963e+1368< 0.1%
 
1.4092821e+1366< 0.1%
 
2.9055907e+1366< 0.1%
 
3.4882134e+1366< 0.1%
 
1.349609e+1265< 0.1%
 
1.5427788e+1364< 0.1%
 
1.342356e+1363< 0.1%
 
Other values (1180380)235168099.9%
 
ValueCountFrequency (%) 
4550001071< 0.1%
 
31290001531< 0.1%
 
32510001201< 0.1%
 
35740001135< 0.1%
 
50580001282< 0.1%
 
64860001751< 0.1%
 
68170001771< 0.1%
 
81510001961< 0.1%
 
92900001341< 0.1%
 
1.04780001e+101< 0.1%
 
ValueCountFrequency (%) 
9.7711797e+132< 0.1%
 
9.7711795e+131< 0.1%
 
9.7554556e+131< 0.1%
 
9.7554536e+132< 0.1%
 
9.7554451e+131< 0.1%
 
9.7554442e+131< 0.1%
 
9.7554433e+131< 0.1%
 
9.7554425e+131< 0.1%
 
9.7554336e+132< 0.1%
 
9.7554233e+132< 0.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.0 MiB
1
1736950 
0
616701 
ValueCountFrequency (%) 
1173695073.8%
 
061670126.2%
 
2020-09-17T22:25:17.995960image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.0 MiB
1
2177479 
0
 
176172
ValueCountFrequency (%) 
1217747992.5%
 
01761727.5%
 
2020-09-17T22:25:18.032861image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

PJ_PORTE
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.0 MiB
1
1451103 
2
691799 
3
210749 
ValueCountFrequency (%) 
1145110361.7%
 
269179929.4%
 
32107499.0%
 
2020-09-17T22:25:18.099717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-17T22:25:18.162539image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:25:18.230333image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
1145110361.7%
 
269179929.4%
 
32107499.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number2353651100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1145110361.7%
 
269179929.4%
 
32107499.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common2353651100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
1145110361.7%
 
269179929.4%
 
32107499.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2353651100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
1145110361.7%
 
269179929.4%
 
32107499.0%
 

PJ_SETOR
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.0 MiB
1
1020903 
2
946508 
3
378545 
4
 
7695
ValueCountFrequency (%) 
1102090343.4%
 
294650840.2%
 
337854516.1%
 
476950.3%
 
2020-09-17T22:25:18.324082image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-17T22:25:18.395890image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:25:18.471687image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
1102090343.4%
 
294650840.2%
 
337854516.1%
 
476950.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number2353651100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1102090343.4%
 
294650840.2%
 
337854516.1%
 
476950.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common2353651100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
1102090343.4%
 
294650840.2%
 
337854516.1%
 
476950.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2353651100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
1102090343.4%
 
294650840.2%
 
337854516.1%
 
476950.3%
 

PJ_IDADE_ABERTURA
Real number (ℝ≥0)

ZEROS

Distinct71
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.794056553
Minimum0
Maximum89
Zeros38132
Zeros (%)1.6%
Memory size18.0 MiB
2020-09-17T22:25:18.571755image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q310
95-th percentile24
Maximum89
Range89
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.523914282
Coefficient of variation (CV)0.9653399653
Kurtosis5.774727618
Mean7.794056553
Median Absolute Deviation (MAD)3
Skewness2.15934169
Sum18344489
Variance56.60928613
MonotocityNot monotonic
2020-09-17T22:25:18.684119image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
229199212.4%
 
32222029.4%
 
51990138.5%
 
11989698.5%
 
41981328.4%
 
61664707.1%
 
71599816.8%
 
81439906.1%
 
101389775.9%
 
91376765.8%
 
11479162.0%
 
0381321.6%
 
12380981.6%
 
13327441.4%
 
14265061.1%
 
15256911.1%
 
16237571.0%
 
17210700.9%
 
18209530.9%
 
19204830.9%
 
21189450.8%
 
20188890.8%
 
23165520.7%
 
22161030.7%
 
24145290.6%
 
Other values (46)1158814.9%
 
ValueCountFrequency (%) 
0381321.6%
 
11989698.5%
 
229199212.4%
 
32222029.4%
 
41981328.4%
 
51990138.5%
 
61664707.1%
 
71599816.8%
 
81439906.1%
 
91376765.8%
 
ValueCountFrequency (%) 
893< 0.1%
 
791< 0.1%
 
722< 0.1%
 
712< 0.1%
 
702< 0.1%
 
692< 0.1%
 
683< 0.1%
 
642< 0.1%
 
624< 0.1%
 
6113< 0.1%
 

PJ_NUM_FUNCIONARIOS
Real number (ℝ≥0)

Distinct101
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.607879418
Minimum0
Maximum100
Zeros591
Zeros (%)< 0.1%
Memory size18.0 MiB
2020-09-17T22:25:18.803888image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile10
Maximum100
Range100
Interquartile range (IQR)1

Descriptive statistics

Standard deviation5.486227972
Coefficient of variation (CV)2.103712286
Kurtosis91.258517
Mean2.607879418
Median Absolute Deviation (MAD)0
Skewness7.879998476
Sum6138038
Variance30.09869736
MonotocityNot monotonic
2020-09-17T22:25:18.929467image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1171815173.0%
 
22051328.7%
 
3868893.7%
 
5668012.8%
 
4595242.5%
 
10329761.4%
 
6319871.4%
 
8219580.9%
 
7195790.8%
 
20139790.6%
 
9123010.5%
 
12112600.5%
 
1599370.4%
 
1163900.3%
 
1353190.2%
 
1448270.2%
 
3040620.2%
 
1637930.2%
 
1937090.2%
 
1835730.2%
 
1726590.1%
 
2525460.1%
 
2221500.1%
 
4016130.1%
 
2116060.1%
 
Other values (76)209300.9%
 
ValueCountFrequency (%) 
0591< 0.1%
 
1171815173.0%
 
22051328.7%
 
3868893.7%
 
4595242.5%
 
5668012.8%
 
6319871.4%
 
7195790.8%
 
8219580.9%
 
9123010.5%
 
ValueCountFrequency (%) 
100695< 0.1%
 
9985< 0.1%
 
9835< 0.1%
 
9737< 0.1%
 
9628< 0.1%
 
9527< 0.1%
 
945< 0.1%
 
9328< 0.1%
 
9260< 0.1%
 
9112< 0.1%
 

PJ_UF
Real number (ℝ≥0)

HIGH CORRELATION

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.775776868
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Memory size18.0 MiB
2020-09-17T22:25:19.035214image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q311
95-th percentile20
Maximum27
Range26
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.111339127
Coefficient of variation (CV)0.7859457943
Kurtosis-0.04350017686
Mean7.775776868
Median Absolute Deviation (MAD)4
Skewness0.9181389931
Sum18301465
Variance37.34846592
MonotocityNot monotonic
2020-09-17T22:25:19.135944image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%) 
132869514.0%
 
22055738.7%
 
31876248.0%
 
41776167.5%
 
51638557.0%
 
61622946.9%
 
71568486.7%
 
81335725.7%
 
91111384.7%
 
10845033.6%
 
11650722.8%
 
12646762.7%
 
13589462.5%
 
14567332.4%
 
15560282.4%
 
16541452.3%
 
17528092.2%
 
18464282.0%
 
19409481.7%
 
20373771.6%
 
21339841.4%
 
22220500.9%
 
23204030.9%
 
24187210.8%
 
2569770.3%
 
Other values (2)66360.3%
 
ValueCountFrequency (%) 
132869514.0%
 
22055738.7%
 
31876248.0%
 
41776167.5%
 
51638557.0%
 
61622946.9%
 
71568486.7%
 
81335725.7%
 
91111384.7%
 
10845033.6%
 
ValueCountFrequency (%) 
27954< 0.1%
 
2656820.2%
 
2569770.3%
 
24187210.8%
 
23204030.9%
 
22220500.9%
 
21339841.4%
 
20373771.6%
 
19409481.7%
 
18464282.0%
 

CANAL_ATENDIMENTO
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.685390485
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Memory size18.0 MiB
2020-09-17T22:25:19.221871image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.185562501
Coefficient of variation (CV)0.7034349083
Kurtosis3.090405236
Mean1.685390485
Median Absolute Deviation (MAD)0
Skewness1.93639531
Sum3966821
Variance1.405558444
MonotocityNot monotonic
2020-09-17T22:25:19.306530image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
1153242465.1%
 
243003618.3%
 
31575356.7%
 
41071064.6%
 
5860043.7%
 
6405461.7%
 
ValueCountFrequency (%) 
1153242465.1%
 
243003618.3%
 
31575356.7%
 
41071064.6%
 
5860043.7%
 
6405461.7%
 
ValueCountFrequency (%) 
6405461.7%
 
5860043.7%
 
41071064.6%
 
31575356.7%
 
243003618.3%
 
1153242465.1%
 

TEMA_ATENDIMENTO
Real number (ℝ≥0)

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.368828684
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Memory size18.0 MiB
2020-09-17T22:25:19.396505image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q36
95-th percentile13
Maximum29
Range28
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.22602995
Coefficient of variation (CV)0.9673141834
Kurtosis2.964315343
Mean4.368828684
Median Absolute Deviation (MAD)1
Skewness1.724336387
Sum10282698
Variance17.85932914
MonotocityNot monotonic
2020-09-17T22:25:19.494249image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%) 
160970825.9%
 
258871625.0%
 
324090910.2%
 
41365615.8%
 
51360835.8%
 
6951354.0%
 
7943264.0%
 
8809183.4%
 
9691032.9%
 
12559182.4%
 
10504022.1%
 
11470762.0%
 
13461342.0%
 
14214020.9%
 
17197260.8%
 
15151670.6%
 
16146060.6%
 
1866520.3%
 
1961320.3%
 
2046240.2%
 
2138210.2%
 
2238110.2%
 
2334100.1%
 
24777< 0.1%
 
25749< 0.1%
 
Other values (4)17850.1%
 
ValueCountFrequency (%) 
160970825.9%
 
258871625.0%
 
324090910.2%
 
41365615.8%
 
51360835.8%
 
6951354.0%
 
7943264.0%
 
8809183.4%
 
9691032.9%
 
10504022.1%
 
ValueCountFrequency (%) 
29243< 0.1%
 
28345< 0.1%
 
27585< 0.1%
 
26612< 0.1%
 
25749< 0.1%
 
24777< 0.1%
 
2334100.1%
 
2238110.2%
 
2138210.2%
 
2046240.2%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.0 MiB
1
2269889 
2
 
83762
ValueCountFrequency (%) 
1226988996.4%
 
2837623.6%
 
2020-09-17T22:25:19.594010image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-17T22:25:19.649862image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:25:19.710665image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
1226988996.4%
 
2837623.6%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number2353651100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1226988996.4%
 
2837623.6%
 

Most occurring scripts

ValueCountFrequency (%) 
Common2353651100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
1226988996.4%
 
2837623.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2353651100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
1226988996.4%
 
2837623.6%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.0 MiB
1
1300681 
2
1052970 
ValueCountFrequency (%) 
1130068155.3%
 
2105297044.7%
 
2020-09-17T22:25:19.797433image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-17T22:25:19.857273image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:25:19.917112image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
1130068155.3%
 
2105297044.7%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number2353651100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1130068155.3%
 
2105297044.7%
 

Most occurring scripts

ValueCountFrequency (%) 
Common2353651100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
1130068155.3%
 
2105297044.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2353651100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
1130068155.3%
 
2105297044.7%
 

INSTRUMENTO_ATENDIMENTO
Real number (ℝ≥0)

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.580884337
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size18.0 MiB
2020-09-17T22:25:19.995902image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum5
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8771976486
Coefficient of variation (CV)0.5548778163
Kurtosis1.322411484
Mean1.580884337
Median Absolute Deviation (MAD)0
Skewness1.400927024
Sum3720850
Variance0.7694757146
MonotocityNot monotonic
2020-09-17T22:25:20.077555image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
1150046463.8%
 
242227417.9%
 
336973415.7%
 
4392591.7%
 
5219200.9%
 
ValueCountFrequency (%) 
1150046463.8%
 
242227417.9%
 
336973415.7%
 
4392591.7%
 
5219200.9%
 
ValueCountFrequency (%) 
5219200.9%
 
4392591.7%
 
336973415.7%
 
242227417.9%
 
1150046463.8%
 

MEIO_ATENDIMENTO
Real number (ℝ≥0)

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.711006857
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Memory size18.0 MiB
2020-09-17T22:25:20.170709image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile9
Maximum18
Range17
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.656998895
Coefficient of variation (CV)0.9800782642
Kurtosis5.874512764
Mean2.711006857
Median Absolute Deviation (MAD)1
Skewness2.395003968
Sum6380764
Variance7.059643126
MonotocityNot monotonic
2020-09-17T22:25:20.252491image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%) 
192719539.4%
 
277429632.9%
 
31628986.9%
 
41223565.2%
 
5939554.0%
 
6687282.9%
 
7344121.5%
 
8341861.5%
 
9283611.2%
 
10269851.1%
 
11261121.1%
 
12194820.8%
 
13177430.8%
 
1462580.3%
 
1560120.3%
 
1638790.2%
 
17560< 0.1%
 
18233< 0.1%
 
ValueCountFrequency (%) 
192719539.4%
 
277429632.9%
 
31628986.9%
 
41223565.2%
 
5939554.0%
 
6687282.9%
 
7344121.5%
 
8341861.5%
 
9283611.2%
 
10269851.1%
 
ValueCountFrequency (%) 
18233< 0.1%
 
17560< 0.1%
 
1638790.2%
 
1560120.3%
 
1462580.3%
 
13177430.8%
 
12194820.8%
 
11261121.1%
 
10269851.1%
 
9283611.2%
 

Interactions

2020-09-17T22:23:13.282239image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:14.231173image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:15.150717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:16.079234image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:17.001766image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:17.877389image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:18.799958image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:19.655670image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:20.567245image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:21.398039image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:22.310150image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:23.255645image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:24.175193image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:25.104710image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:26.029235image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:26.997663image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:27.859308image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:28.787451image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:29.638175image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:30.543754image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:31.350598image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:32.242179image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:33.173726image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:34.088243image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:35.004791image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:35.939324image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:36.850854image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:37.715638image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:38.640132image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:39.526762image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:40.428350image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:41.228236image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:42.121821image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:43.048378image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:43.960902image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:44.880443image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:45.801979image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:46.715536image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:47.581981image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:48.499801image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:49.357470image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:50.267070image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:51.072922image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:51.971479image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:52.908973image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:53.836635image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:54.842944image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:55.768976image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:56.682025image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:57.553694image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:58.472238image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:23:59.326952image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:00.245495image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:01.051447image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:01.957916image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:02.859537image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:03.753115image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:04.646757image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:05.541333image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:06.431983image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:07.292681image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:08.185262image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:09.009091image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:09.893824image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:10.686703image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:11.579316image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:12.535861image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:13.452307image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:14.368855image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:15.286541image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:16.193942image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:17.071594image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:17.998122image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:18.848810image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:19.755418image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:20.556243image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:21.453876image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:22.482126image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:23.397644image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:24.347140image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:25.264437image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:26.259701image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:27.141373image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:28.035949image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:28.972476image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:29.871042image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:30.679883image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:31.604405image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:32.577926image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:33.557265image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:34.534649image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:35.490095image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:36.450559image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:37.366109image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:38.309555image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:39.180258image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:40.178591image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:40.991383image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:41.915685image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:42.878290image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:43.796655image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:44.785012image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:45.792317image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:46.719837image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:47.582532image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:48.495089image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:49.352795image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:50.255415image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:51.061259image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:51.959824image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:52.895379image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:53.807914image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:54.715487image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:55.631005image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:56.539576image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:57.397289image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:58.330515image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:24:59.225122image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:25:00.120727image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:25:00.926156image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:25:01.827163image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-09-17T22:25:20.364926image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-09-17T22:25:20.610527image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-09-17T22:25:20.995406image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-09-17T22:25:21.242744image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-09-17T22:25:21.453182image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-09-17T22:25:03.912619image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:25:07.154468image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T22:25:13.906235image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

CPFPF_SITUACAOPF_SITUACAO_RFBPF_IDADEPF_GENEROPF_UFCNPJPJ_SITUACAOPJ_SITUACAO_RFBPJ_PORTEPJ_SETORPJ_IDADE_ABERTURAPJ_NUM_FUNCIONARIOSPJ_UFCANAL_ATENDIMENTOTEMA_ATENDIMENTOABORDAGEM_ATENDIMENTOCATEGORIA_ATENDIMENTOINSTRUMENTO_ATENDIMENTOMEIO_ATENDIMENTO
04.650267e+0911451.0132.055678e+1311126113151212
11.306666e+0911310.037.338788e+1201311313121212
22.780436e+1011591.043.311693e+131112114111212
31.200526e+0911390.012.616854e+131122451131111
47.542230e+1011431.0143.354560e+1301111114111212
54.166229e+1011490.051.720109e+1311128151131232
69.174883e+0911390.022.663826e+131112412451211
78.280314e+0911461.093.249453e+131112119111232
81.214082e+0911340.022.152293e+130111612111216
96.171112e+0911211.0123.453417e+1311121112121232

Last rows

CPFPF_SITUACAOPF_SITUACAO_RFBPF_IDADEPF_GENEROPF_UFCNPJPJ_SITUACAOPJ_SITUACAO_RFBPJ_PORTEPJ_SETORPJ_IDADE_ABERTURAPJ_NUM_FUNCIONARIOSPJ_UFCANAL_ATENDIMENTOTEMA_ATENDIMENTOABORDAGEM_ATENDIMENTOCATEGORIA_ATENDIMENTOINSTRUMENTO_ATENDIMENTOMEIO_ATENDIMENTO
23536414.062365e+1011540.0102.452559e+13011241101121232
23536424.171359e+0911311.081.664272e+131121838231121
23536439.114684e+0811380.0113.094719e+1311132111121111
23536443.556089e+1011621.012.244326e+131113511211212
23536456.024705e+1011320.0181.713113e+1300118118151111
23536461.682244e+0911341.0218.898144e+1201321314211111111
23536474.244522e+0811341.032.664215e+131121453372141
23536488.689250e+1011460.0212.390253e+1311114121121113
23536499.989666e+1011381.0102.462663e+1311314510121111
23536508.505110e+0911420.029.302682e+1211211252211121

Duplicate rows

Most frequent

CPFPF_SITUACAOPF_SITUACAO_RFBPF_IDADEPF_GENEROPF_UFCNPJPJ_SITUACAOPJ_SITUACAO_RFBPJ_PORTEPJ_SETORPJ_IDADE_ABERTURAPJ_NUM_FUNCIONARIOSPJ_UFCANAL_ATENDIMENTOTEMA_ATENDIMENTOABORDAGEM_ATENDIMENTOCATEGORIA_ATENDIMENTOINSTRUMENTO_ATENDIMENTOMEIO_ATENDIMENTOcount
14177.514879e+0911311.073.063645e+1301112176111214
7973.683213e+0911320.082.220708e+13011151821212353
8864.246570e+0901310.082.171820e+13011251821212153
35656.071486e+1001490.083.288509e+13011211821212353
03.666174e+0611321.062.936440e+1301132164311112
15.382726e+0611541.023.611943e+1301333032221212112
26.635733e+0611530.022.001660e+13011161221211212
31.479726e+0711330.031.445328e+1311239133321412
41.529021e+0711300.0233.600925e+130121052343122102
52.514673e+0711540.022.051776e+13111361221212112